How to Talk to Robots
A Girls’ Guide to a Future Dominated by AI
Tabitha Goldstaub

Copyright

4th Estate

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This eBook published in Great Britain by 4th Estate in 2020

Copyright © Tabitha Goldstaub 2020

Illustrations © Jess Wade 2020

Tabitha Goldstaub asserts the moral right to be identified as the author of this work

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Source ISBN: 9780008405878

Ebook Edition © September 2020 ISBN: 9780008328221

Version: 2020-09-21

Dedication

For all the women I’ve met on the way

CONTENTS

Cover

Title Page

Copyright

Dedication

Foreword

Key Words & Phrases

1. What is AI?

2. A Potted History of AI

3. How Does AI Work?

4. How Not to Talk to Robots: The Potential Risks

5. Why Talk to Robots: The Potential Rewards

6. AI and the Coronavirus Crisis

7. The Interviews

8. Actions

9. What Next?

Acknowledgements

About the Author

About the Publisher

Me, Tabitha Goldstaub

FOREWORD

Growing up, my brother and I played Gameboy incessantly. Yoshi’s Dream World, Mario Kart Racers, Tekken. The gaming world was full of beautiful pixelated landscapes. I loved the thrill of the win. For me, gaming was a non-gendered space. My household was one dedicated to fashion: my mother was a magazine editor, my father a textiles merchant. So I grew up sitting on the floor of fashion shows, but often more entranced by my Gameboy than the clothes on the catwalk.

In 1995, when I was ten, my parents bought me one of the very early home computers. It was proudly positioned in the kitchen, where I would spend hours absorbed in the Microsoft Paint program, creating personalised desktop backgrounds for each member of my family. I felt like a tech Picasso – computers were for art not engineering.

At school, the door to the Internet was Ask Jeeves, a British butler who fronted the search engine, and my guide to Word and Excel was Clippy, Microsoft’s helpful paperclip mascot. I remember having a Tamagotchi that was neither boy nor girl. All this is to say that I was happily unaware of the notion that gender could ever play a part in technology. It was not a male or female thing: it was my thing.

Cut to a few years later, and during mandatory GCSE IT classes, I was bored out of my brain. We were being shown how to run a doctor’s reception and were using mail merge to access patients’ data and send emails. I was not inspired. Thinking about it now, it’s little wonder I was unable to see technology’s true potential. I knew it could make a job as a receptionist more efficient and less repetitive, but what if my teachers had explained to me that tech could help predict which of my patients would need life-saving treatment? Maybe I would have gone on to study computer science. Instead, I followed in the footsteps of my grandpa and set about joining the advertising world. I was genuinely fascinated by the clever people behind the slogans, billboards and TV ads that captivated mine and my friends’ impressionable minds. At that point I was enamoured and not yet fearful of their power.

Before university, I was working in a pub when brand new fully computerised till systems were installed. The manager decided to train the men on the tills first, which meant they became better faster and so were assigned more shifts behind the bar. The women ended up waiting tables. I didn’t even stop to think about whether this division was fair – I thought computer stuff wasn’t for me, and I think I just accepted that the guys should do the tills. But the consequence of this mindset meant I inadvertently accepted lower pay: waitresses earned less than bar staff, and I ended up hating a job I had come to love. What on earth had happened to me in those interim teenage years?! I still don’t know at what point it became the norm that the world of computers was a male one.

By the time I arrived at university to study advertising, new developments with the Internet had set the world alight. Ask Jeeves was no longer the gatekeeper to information – now I could research anything and share it immediately. Users had also started communicating with each other directly, and I remember waiting for my university email to grant me access to The Facebook (as it was known back then), which was in its infancy as an American college social-networking platform. The nature of communication was changing, and my chosen route had begun to feel outdated. People’s minds were not going to be made up by old-school advertising. I was falling in love with the Internet, with the endless opportunities it presented and its immediacy and scale.

Over the course of those three years, I chose modules that would enable me to redefine my relationship with computers. This included how to code. But I found that my dyslexia and my lack of confidence held me back. I was, quite honestly, hopeless. My real ‘aha’ moment came when I was introduced to Squarespace, a tool that meant I didn’t have to write code in order to create a webpage and reach people through the Internet. There were companies and products being released every day that meant people like me, who didn’t get on with coding, could still benefit hugely from opportunities available on the web.

All the advertising agencies at which I interned during my degree treated digital campaigns as the second division, and often relegated women to the same position. I felt pretty downtrodden. But the start-up world seemed to have a different vibe. From afar, I’d watched Martha Lane Fox, the co-founder of lastminute.com (who you’ll read about later), take equal billing with her male co-founder as they reached millions online. It seemed like a level playing field, and this excited me endlessly.

So, in 2008, on my hunt to either start a business or work for a start-up, I met Charlie Muirhead, the CEO and Founder of t5m, a new style of digital studio that co-produced awesome online video content with celebrities. I joined as an intern, learning how to add words and codes to propel each video to the top ranks of YouTube and then distribute them across the Internet. When I was promoted to managing a team of other recent graduates, we soon realised that the more laborious, mechanical aspects of our job had to become automated for full efficiency – and to avoid us going mad! I relished the opportunity to work more closely with the engineering teams to make this happen.

During this time, the production part of the business had some great successes, but our part-human part-software team were truly flying. The business pivoted, and we concentrated solely on building the technology to enable us to distribute other people’s videos. Seeing those online views rack up was everything to me. I was hooked: how could we get more views and faster?

This company, Rightster, grew to 250 people across twelve countries, and I was later recognised as the co-founder of what was by then the largest online video distribution company outside of America. We live-streamed Kate and William’s wedding, over 2,000 fashion shows, the BRIT Awards and many other high-profile events. But the work still felt very manual – we had to work with thousands of websites in order to get the millions of views that our clients wanted. Even with an awesome seventy-strong tech team, I believed there had to be a way to escape endlessly copy-and-pasting the code that transferred the video from the cloud to the online website.

This is when I first heard about Data Science, Machine Learning and, eventually, AI. I thought it was a miracle. Sleepless nights could be a thing of the past – the machines could take over! But of course, it wasn’t that simple. Charlie sent me on a mission to investigate, and I spent an entire year, and a significant amount of the company’s money and my colleagues’ patience, trying to harness this new technology. The dream was to use AI to get the right video to the right person at exactly the right time. This would then make them click on a link. We needed this to be done automatically, without having to predict which sites should embed the video, or which video to serve to which user. But, frustratingly, we didn’t achieve the necessary financial results fast enough, and people lost faith. Back then, there were only a few companies who knew how to build AI systems, and it was nigh on impossible as a layperson to recognise the hidden gems. I learned the hard way that AI wasn’t magic, and it would take a lot more than just a passion for it to work.

This experience shaped our next big leap. After Rightster went public and acquired similar businesses in the field it was time for us to leave. We agreed to dedicate our time to better understanding AI and learning what it meant for other companies. Lucky to be in London and surrounded by some of the best universities in the world we hosted dinners for big brains and debated the hottest topics of the day. It was clear that AI was a paradigm shift that was going to change the whole of society, not just our business. Now, this may sound dramatic but as you’ll see later in the chapter on history this is not a new phenomenon or a novel revelation – AI has been around since the 1960s – but it was a concept to which the business world was only just waking up and it was the most exciting time. Our findings led us to setting up CognitionX.

CognitionX now employs over fifty people and provides a platform that connects businesses with questions about AI, or other emerging technologies, to the right expert in their own organisation, or thousands of experts in the wider community, who can provide them with answers and so help them navigate the complex AI landscape. For example, we’ve written reports for the Mayor of London’s Office, where we analysed the AI ecosystem in order to help develop policy to encourage more companies to set up in the city. It’s an honour to get to work with such incredible brains, be they from big corporates such as HSBC or from local government. Each year we hold a festival in London called CogX, where over 30,000 visitors from industry, government, civil society and academia come together to discuss how to forge a brighter future.

My passion for the AI community meant I spent every waking minute untangling the key questions the community faced. I cherished every opportunity to connect people with problems and in 2018 I was appointed by Matt Hancock and Greg Clark, two Secretaries of State in the UK Government, to assemble a team of top experts in AI to form a Council. The group is responsible for supporting the government of the day and its Office for Artificial Intelligence, currently helmed by Sana Khareghani, to ensure that future decisions about AI enable the UK to flourish and for all of society to benefit.

It’s fair to say that I threw myself into the world of AI and tried to talk to every machine going. Whether in a meeting or with my friends, being interviewed on BBC Breakfast, I’d take every opportunity to explain why AI was the best thing to happen to humans – I was overly optimistic to say the least. But things began to shift when I realised that this technology would affect different people in different ways. I started to read widely and deeply on the topic, and I woke up to the reality that it could potentially have seriously negative effects for many women. This led me to gather women and host events looking at ‘Why we needed Women in AI’, in the mission to understand the scale and depth of the challenge I saw before us.

I was giving a talk a few years ago at an event hosted by the achingly cool Riposte, a magazine for women in creative industries. I looked around the room and realised I couldn’t give my usual positive speech about AI and business. These women were at the top of their respective games, and I needed to explain why AI would matter to them. In fact, I welled up as it dawned on me how many women’s careers could be destroyed by smart machines if they weren’t aware of how AI was set to change all the rules. This book is one small step in hopefully arming those women – and you.

Getting Started

I’ve been fortunate enough to have a front-row seat as the world is transformed by new technology, and my aim here is to give you one too. I want to share with you the trials that are coming our way, as well as the potential that exists within AI for positive change. The way I see it, this new wave of technology could be a tsunami that knocks you down, or it could be the wave that we ride together to a brighter future. The moment I began to truly understand this, I knew I had to share what I’d learned about its possible risks as well as its rewards – and why it is that women could be more likely to suffer the negative effects. So it’s important to stop seeing tech as ‘boring’, ‘scary’ or ‘for someone else’. I’m not a scientist, engineer, developer or techie. It takes me a long time to understand technological ideas because they’re mostly founded in complex mathematics. It was a really liberating moment when I realised that I didn’t need to understand the precise inner workings of AI machines in order to understand the ramifications of this technology. All you need is to get a good grasp on how to adapt and thrive in this new world and what you can do to support others to do the same.

At this point, I think it’s important to stress to you that the world of AI is rapidly changing all the time. It’s impossible for me to condense the mass of compelling and complex ideas out there. Instead, this book is for all of us who need a good enough understanding to get by; not the intimate technical details. Consider it a patchwork quilt of sorts, an introduction to AI and an invitation to further learning. At the heart of this book is a chapter of interviews with a cross section of women who have informed my thinking. So grab a biro and get ready to add your own thoughts to these pages. Scribble across and circle areas you want to look up later. I often mention articles and academic papers – you can use a search engine to find out more about what they say. If you’re reading on a Kindle or listening to an audiobook, maybe you can use voice notes – or even have an AI transcribe them!

Continue your learning with the further reading and watching at the back of this book, check the suggestions I’ve included, attend events and test new theories yourself. I hope How to Talk to Robots will give you the confidence to continue your journey of discovery and find your rightful place in the world of technology. This might well be the first book about ‘robots’ you have picked up, but I’m hoping it won’t be the last.

KEY WORDS & PHRASES
to help you use this guide

Here are a few of the regular words and phrases you might stumble across while reading this book. Some are used widely, others are more niche. Don’t feel as if you have to read this section before you get stuck into the chapters – it’s just here for you to use as a reference any time you spot something that needs a quick definition. If anything else is unclear, why not ask Siri or other voice-activated assistants on your phone, who will use a combination of speech recognition and natural-language processing to help you out.

HOW TO TALK TO ROBOTS: I titled this book How to Talk to Robots because How to Talk to an Artificial Intelligence System wasn’t quite as snappy, and I wasn’t sure I’d get your attention. In most cases these AI systems won’t have a physical ‘body’ so please don’t confuse the two. Throughout the book, we’ll keep referring back to this challenge of interacting with AI – all I really mean is how to engage with products, either physical or digital, that use artificial intelligence. And not all robots use AI either!

ARTIFICIAL INTELLIGENCE (AI): This is a tricky one because the definition of AI is hotly debated. At its most basic, AI describes a computer system able to perform tasks normally associated with human intelligence, like reasoning or learning. Within this wide definition of AI, there are two important distinctions.

1. ARTIFICIAL NARROW INTELLIGENCE (AI):

Artificial Narrow Intelligence is an umbrella term for a system that can perform a single intelligent task. It is built for a single objective, such as automating image sorting, or text generalisation. This is the kind of artificial intelligence researchers are currently able to build.

2. ARTIFICIAL GENERAL INTELLIGENCE (AGI):

Sometimes called superintelligence, AGI refers to the ideal realisation of the field of artificial intelligence, where machines would be fully able to learn, reason and act for themselves, all at once.

AFFECTIVE COMPUTING: An area of research in robotics and AI focused on thinking about and simulating emotions and feelings in machines. In addition to computer science, affective computing can draw on ideas in information studies, psychology, philosophy and others.

ALGORITHM: This is a messy one! An algorithm at its most simple is just any set of rules a computer follows to solve a problem. But when I talk about algorithms in this book, I am referring to AI algorithms, or the instructions the AI takes to perform its task. These are often also called models when the algorithm has already been trained with data.

BIG DATA: A way of describing extremely large datasets by using a computer to analyse and reveal the patterns, trends and associations in one set.

COMPILER: A compiler is a system that translates code into a mode that computers can process.

DATA: Is made up of individual units of information. Although the terms ‘data’, ‘information’ and ‘knowledge’ are sometimes used interchangeably, in the case of AI it’s what people use to describe the information that an AI system is trained on or given to learn from.

DATASET: A collection of data pulled together for analysis or study.

DEEP LEARNING: Is a type of Machine Learning where the computing system mimics the structure of the human brain to learn from experience. This is why the structure of a deep learning system is often called a NEURAL NETWORK.

EXPERT SYSTEMS: A kind of computer system that uses databases of knowledge to make decisions, much like a human expert. This term arose in the 1970s, had its boom in the 1980s, but is used less often today.

FILTER BUBBLE: Personalised algorithms can create a ‘bubble’ effect so that a Web user only encounters information and opinions that reinforce their existing interests or beliefs.

GENDER DATA GAP: Caroline Criado-Perez uses this term in her book Invisible Women to describe the ways that data sets are often male-biased. This means the conclusions drawn from that data are less likely to reflect the needs and experiences of women, and women of colour in particular.

NATURAL LANGUAGE PROCESSING (NLP): A diverse field of study within artificial intelligence that asks questions about and helps machines process human language.

NEURAL NETWORKS: A layered structure of algorithms used in deep learning systems. This is why deep learning and neural networks are terms commonly intertwined when talking about how AI works.

MACHINE LEARNING: A kind of Artificial Narrow Intelligence where a machine learns to make decisions from data and experience, instead of it being explicitly told how to perform a task. Machine learning is called DEEP (MACHINE) LEARNING, when there are multiple steps in the decision-making process. There are three major types of machine learning: supervised, unsupervised and reinforcement.

STEM: This is a shorthand way of referring to the fields of science, technology, engineering and mathematics.

TRAINING DATA: This is the data that contains labels for different groups so the machine can learn the characteristics of each group. This almost always forms the basis for supervised machine learning.

1
WHAT IS AI?
And why should it matter to you?

Have you used a Snapchat filter to see what you’d look like as a baby? Is your inbox delightfully clear of spam emails? Are your photos on Facebook automatically tagged with the correct friends? Is the autocorrect on your phone so good you think it’s psychic, or so absurd that you text it to your best friend anyway? Have you had a full-blown conversation in French using one of the speech-to-speech translation apps? Are you guided – or bitterly disappointed – by Netflix’s film and TV viewing recommendations? If you answered yes to any of these questions, you are already interacting with artificial intelligence.

The AI that many people use every day is getting more accurate all the time. Take Google Maps. Originally, this app just got you from A to B. Then it helped you get from A to B with the least amount of traffic. Now it asks how busy your tube or bus ride was, then translates this information to let you know if there’s usually only standing space at particular times. Then Google Places appeared, showing you inside buildings, restaurants and points of interest across the world. It can make recommendations for where you might like to visit. If you use a Google calendar to record your dentist appointment, it will automatically personalise your map to include where your dentist is. It’ll even suggest when to leave your meeting in order to get to the next one on time. Last night, Google Maps dropped a pin when I parked my car to remind me where it was after I returned from dinner. Initially I thought this was spooky but then I was grateful because I always seem to forget. By the time you’re reading this, who knows how much more advanced the simple map will have become. We’ll discuss later the risks of relying too much on this new technology; we’ll also talk about who has access to this technology and who does not (and why that matters). For now, if you do use a smartphone, just hold in your mind all the ways that your day-to-day is being affected by tech in the palm of your hand.

AI is automating everyday tasks at an unprecedented pace. As we acquire and learn how to use more new digital devices, we’re also constantly upgrading our dependency on AI. Few people are oblivious to how this technology has firmly slotted into our lives, but most of us don’t appreciate that the rules of this tech are constantly shifting and evolving. We’re now living in an era where machines are taught to learn and adapt without human intervention, and this has some serious ramifications that we’ll explore over the course of the next few chapters.

The many functions of AI will continue to impact every aspect of our daily routines, but as well as helping us to avoid traffic or introducing us to new music, AI systems will detect disease, reduce energy consumption, decide which of us receives an approved loan, power vehicles to be autonomous and self-driving and both inform and control our news and advertising feeds. AI has the potential to unlock a future where humans live longer, healthier, happier lives. It could change the nature of much work: taking over many repetitive, boring tasks and freeing humans up to spend time on creative, fulfilling projects. This could dramatically change common conceptions of how much of our lives we need to spend on work, allowing us all to spend more time with each other and on our relationships, or on whatever it is that makes life worth living for you.

But there is a much scarier alternative. The flipside to this technology is that it could make life a lot worse for a lot of us, and especially the most vulnerable: it could widen the poverty gap, further increase inequality, reduce diversity and re-entrench many of the structures that keep some people down, no matter what they do. Anyone who tells you otherwise is not telling you the whole story.

The reality of AI, in its current state, is that it adopts the truths of its creators: humans. AI-driven machines learn from data that humans feed into their systems, meaning that they can also learn the social norms that many of us are desperately trying to escape or eradicate. For example, what if they are programmed to accept the existing pay gaps, or the idea that a woman’s place should always be in the home? If misogyny and unconscious or conscious bias is codified into the next wave of technology, we are all exposed to a less fair, less equal future. It gives me the shivers just thinking about it. So, although AI has enormous potential to improve our lives, there is the risk that rather than empowering women it may continue to compound existing stereotypes. And, as we’re going to see below, the evidence that we have suggests that the risk is already becoming the reality. We have to protect our rights and fight against oppressive gender constructs becoming codified, because if they are, then whatever progress women have made over the past century could very easily be wiped out. We need to do so much more than become merely competent responders to AI.

The first step here is to accept that to thrive we must learn to live and work alongside machines. I don’t want women to be more at risk of losing their jobs to a machine because there wasn’t enough material out there to prepare them for using it. But don’t worry, as you’ll read time and again from some of the women interviewed here, this does not mean we have to become coders, statisticians, designers, or engineers. On the flipside, we certainly can do all these things and in fact, many of the pioneering early coders were women.

No one will be unaffected by AI. Are you at school deciding what work experience to do? Are you in university and making plans to enter the world of work? Are you in an office or in retail? Maybe you work in a hospital as a nurse or a porter or a doctor. Or are you a banker, an accountant, an advertising executive? Are you a part of the gig economy? AI technology, as you perhaps know all too well, is already permeating your workplace. The challenge now is to work out how to make sure it helps you rather than undermines you.

It’s widely publicised that only 13 per cent of engineers and data scientists working in the West are women, and so I hope some of you go on to change this shocking statistic. But what this book is really about is inspiring you to get interested in tech, feel comfortable working alongside it, leverage it, use it, be enabled by it, know how to be heard and have a stake in how technology is built and deployed. It’s a pragmatic guide for the uninitiated.

There are ways that you can be instrumental to the building of the AI systems. Just think about how many different steps there are when building a product. Companies are going to need people who love languages to give AI a voice; historians and philosophers to give AI context; designers and artists to give AI personality and an interface; and product managers to ensure the AI is fit for purpose. The number of non-technical roles will become crucial as we try to build machines that think and act like humans. AI should be democratised not professionalised. All this power should not lie in the hands of the few, nor should it stay only in the hands of men who currently make up the dominant percentage of the tech workforce.

OK, sounds good, but what exactly is AI?

In order to answer this question, I called on Karen Hao. Karen is a journalist, storyteller, engineer and for the past few years she’s been the woman who has explained complicated concepts to me via the MIT Technology Review magazine. She has made what hundreds of other people have tried to explain to me before just click into place. She’s always been good at finding novel ways of communicating: as well as an AI expert, she’s also passionate about the environment and as an undergraduate, launched a fashion show entirely made of rubbish! She’s a woman after my own heart in more than one way.

What are the fundamentals of AI that everyone should understand – and why does it often seem confusing?

Well, in the broadest sense, AI refers to a branch of knowledge that strives to recreate human intelligence within machines. In the ideal realisation of this goal, such machines would be able to learn, reason and act for themselves, mimicking the ways we as humans, or a pet dog, might do so. They would take in information from the rapidly evolving world, process it and then figure out how to respond based on prior experience – and they would supposedly be able to do so much faster and on a far greater scale than any individual human could.

Karen Hao

This is the dream of artificial intelligence: to make these super capable machines that can put their ‘minds’ together with ours to solve some of the world’s most complex problems: climate change, poverty, hunger – things we haven’t been able to wrap our heads around on our own. I like to think of this dream as Janet from NBC’s comedy series The Good Place. She’s a kick-ass, fully autonomous and highly intelligent agent that helps her human counterparts be better versions of themselves.

Part of the reason why AI gets confusing is because the term can often feel like it refers to two completely different things. We now know one of them – the vision of the field, which evokes something closer to what we see in science fiction. But as you’ve already intuited, today’s AI systems are nothing near the clever, autonomous agents that I just described. They’re simpler and less capable, able only to perform specific tasks such as adding dog ears onto your Snapchat photo, ranking your content on Facebook or recommending new songs on Spotify to match the genre you like.

In the field, these two versions of AI have been given different names. The dream is often called ‘artificial general intelligence’, or AGI, while the reality is sometimes referred to as ‘artificial narrow intelligence’. People usually call artificial narrow intelligence AI. But these definitions are often combined into one in popular culture, causing people to think that the AI we have today is far more advanced than it really is.

There is another element that complicates the whole thing further. Because AI researchers are constantly pushing the boundaries of the technology, the definition of present-day AI also changes over time. What might have been considered AI thirty years ago is no longer really considered AI today. And what we know as AI today has only been the working definition for less than a decade. Certainly, among experts there’s a considerable amount of debate about what constitutes AI and what it will be capable of in the future. In fact, one of the fiercest debates is about whether AGI is even possible!

When you add all that up, it goes without saying that the notion of AI is constantly being tweaked, debated, probed and refined. Don’t let that overwhelm you. Instead, take it as an invitation: what AI is and where it’s going is ultimately shaped by people. That means you can have a role in influencing the technology, which I hope you find as hugely exciting as I do.

So what do you think the most common misconceptions are about AI?

One of the biggest things that confused me when I first began covering the field is the difference between robots and AI. The two are clearly interrelated, yet they are not the same thing. As it stands now, AI specifically deals with software. Robotics, by contrast, deals with hardware. Think of it as your brain versus your body. Your body relies on instructions from the brain to move, but your brain also relies on your body to experience and sense its surroundings so it can learn about the world.

Of course, as mentioned in the introduction, AI doesn’t always come physically embodied in a robot. More often than not, it exists as a hidden algorithm on your favourite websites or apps. Similarly, robots aren’t always powered by AI. Instead, their actions could be dictated by software that executes a series of hard-coded rules. In those instances, the robots can neither learn nor adapt to unexpected circumstances, so they are usually confined to perform rote tasks in unchanging environments such as a manufacturing floor.

When AI and robots combine, that’s when the interesting stuff happens. It’s no longer only about efficiency but breaking new ground as well. Self-driving cars, for example, are a product of this union.

What happens if we rely too much on AI?

The most popular products we use are developed and implemented by tech corporations with profit motives. That’s why social media and streaming platforms can be so addictive: the machine-learning algorithms are all pushing us to stay on these platforms for just a little longer. That’s also why ads can sometimes turn predatory: the algorithms get so good at knowing our weaknesses that we end up spending more money than we should have. As algorithms learn more and more about you, implications regarding privacy become a serious concern.

It’s important, therefore, every once in a while to think through how algorithms are impacting your life. What do you like about how they’ve changed it? What do you not? Ultimately, you are in control of the way you interact with them. You could choose to abstain from them entirely: Facebook, for example, gives users the option to turn off automatic photo-tagging. Or you could learn to hack it so that it does different things: if YouTube is showing you too many similar videos, purposely watch some radically different ones to reteach the algorithm about what you want.

What’s your one piece of advice for the reader?

Command algorithms; don’t let them command you! And you’re already one step ahead of the game because you’re reading this book.

As Karen has explained, AI has been around for a lot longer than it first appears. The next chapter takes a closer look at the history of AI. It might not be what you’re expecting …

2
A POTTED HISTORY OF AI

One of the many fascinating aspects of AI is that although it still seems super futuristic, its roots lie in classical thinking. Since we humans have always been partial to making things easier for ourselves, we’ve long been pretty adept at designing machines to increase efficiency. Combine that disposition with some killer mathematics and it’s little wonder that AI has been a twinkle in our eye for quite some time.

The quest for artificial intelligence as we know it began over seventy years ago with the idea that computers would one day be able to think as humans. As so much of the history of AI stems from early computers, we’ll start off by looking at where it all began. You’ll soon notice that early AI was influenced by many different disciplines – yes, mathematics and engineering play their part, but so do biology, game theory, psychology and philosophy. AI is not just the domain of computer scientists. It has always been, and will continue to be, an arena that explores what it means to be human and how we live our lives.

AI’s interdisciplinary nature means that there are many ways to trace its history. This chapter is one way and is not exhaustive. Instead, I wanted to focus on some of the major moments and shine a light on some of the women who’ve been so chronically overlooked along the way. There is a long tradition of women translating or communicating science, partly because of being excluded in some way from the mainstream of conducting it. In fact, I’ve searched far and wide for all the women who played a role in the development of AI, but many were simply never recorded in the archives. This is why I’ve called this chapter a potted history: because I’ve chosen the accounts that most inspired me. Consider this just one way of telling a very complicated story. A historian of science at the University of Cambridge, Patricia Fara, summarised this perfectly when she said to me: ‘Broadening what counts as science’s history entails recognising and crediting women’s involvement.’

I’ve ordered this chapter as a timeline of individuals, but it’s really important to note that the history of AI, as any history of innovation, is never that straightforward. The history of computing in particular is often documented as a series of male geniuses appearing one after another, which simply isn’t the case. So often when history is told as ‘a series of geniuses’, women, particularly women of colour, are erased from the narrative. Among the many reasons for this is the big one: traditionally, the people writing history are the same people who hold the power – white men. When new inventions or ideas shake up our way of thinking or doing, it’s always a result of many people working together, forming communities and pushing things forward – not just one individual, inspired though they may be. So please remember as you read this that it’s networks of people, not individuals on their own, who’ve brought us to where we are today.

Pre-Twentieth Century
The Antikythera Mechanism (205 BCE)

Mechanical machines have been performing complex functions for much longer than you would think. Salvaged from the depths of the Aegean Sea, the Antikythera mechanism has been described as the world’s oldest computer. This strange thing has thirty-odd gearwheels and countless astronomical inscriptions, and it dates to 205 BCE – over 1,200 years before mechanical clocks first appeared in Europe. International experts have decided that it must have been part of an intricately engineered machine for crunching the mind-boggling mathematics needed to model the positions of the sun and moon. The fact that it not only monitored but also seemed to have forecast solar cycles is of course exciting for astronomy enthusiasts, but it also makes it part of the long history of artificial intelligence. Experts still don’t know its exact purpose – it could have been a teaching tool, or used for navigation, but I like to think that it was an early version of the Clue app, tracking women’s menstrual cycles. The likelihood of this is, sadly, zilch. As you’ll see, technology developed in patriarchal societies rarely takes the needs of women into account.

The Analytical Engine & Ada Lovelace (1815–1852)

The partnership between Ada Byron, Countess of Lovelace, and Charles Babbage in the 1830s was perhaps the true birth of computer science.

As a child, Ada Lovelace was taught mathematics by her mother Lady Byron, who hoped to keep her well away from the poetry (and temperament) of her father Lord Byron. Babbage was the inventor of a machine he called ‘the Analytical Engine’, an early computer designed to complete mathematical functions. When Lovelace first met Babbage, he asked her to translate into English an account of his Analytical Engine written by the future prime minister of Italy. She made copious notes on the paper and proposed an algorithm for the Analytical Engine to calculate a sequence of Bernoulli numbers that blew Babbage away. This algorithm is arguably the first true piece of computer code. Though it was a century before the dawn of the computer, Lovelace went on to imagine a machine that could be programmed to follow instructions. But as well as calculating, it would also create. She anticipated a machine that ‘weaves algebraic patterns just as the Jacquard loom weaves flowers and leaves.’

Although the computer she wrote about was never built, it was Lovelace’s imagination, application and appreciation of maths and mechanics that earned her the title of first computer programmer and the epithet ‘the Enchantress of Numbers’. It’s exactly this kind of imagination that enables researchers, academics and entrepreneurs worldwide to push boundaries and make new discoveries with artificially intelligent machines today. I love knowing that it was Ada Lovelace, a woman well before her time, who sparked this line of enquiry.

Early Twentieth Century
Alan Turing (1912–1954) & Joan Clarke (1917–1996)

Alan Turing was an English mathematician and computer scientist and is now considered the father of theoretical computer science and artificial intelligence. Turing studied mathematics at Cambridge, and it was there that he first started thinking about the possibility of an intelligent computer. In the 1930s, Turing went to the USA and worked with John and Klara von Neumann – remember their names, we’ll come back to them shortly! – who were also fascinated by the idea of possible computer intelligence. Shortly before the Second World War broke out, Turing returned to the UK with everything he’d learned and took up a position with the Government Code and Cypher School. You might recognise his name from the 2014 film The Imitation Game, a dramatisation of how during the Second World War he cracked the Enigma Code, which was used by the Germans to send commercial, diplomatic and military communications, and so helped the Allies to defeat the Nazis. But he didn’t work alone. At their base, Bletchley Park, Joan Clarke rose from clerical work to deputy head of operations in Hut 8, becoming its longest-serving team member. To be promoted and receive her initial pay rise, Clarke’s title had to be changed to ‘linguist’ as the Civil Service had no protocols in place for a senior female cryptanalyst. Clarke was tasked with breaking navy ciphers in real time, resulting in almost immediate military action. The secrecy of Bletchley Park means that many of her achievements remain unknown today.

What we do know is that the Bletchley Park code-breaking operation was made up of nearly 10,000 people and about 75 per cent of these were women. Very few women there have been formally recognised as cryptanalysts working at the same level as their male peers. Thankfully, in more recent times, Mavis Lever, Margaret Rock and Ruth Briggs have also been named. I like to think I would have been friends with Sarah Baring, as she combined her work for Vogue with that of her duties as a linguist in Hut 4 at Bletchley Park.

As Professor Sue Black, the woman who campaigned to keep Bletchley Park open, explains, ‘the lifeblood of some of Britain’s bravest and most inspiring citizens pulsed through Bletchley’s veins at the most crucial turning point of the war. Here, thousands of men and women contributed to the effort that saved our nation and inspired future generations with their work in the fields of computing and technology.’ What’s great is that you can now go and visit these huts and put yourself in those women’s shoes.

ENIAC, John & Klara von Neumann (1903–1957 / 1911–1963) & Jean Bartik (1924–2011)

Around the same time in America, husband and wife team John and Klara von Neumann were using their respective skills to build early computers to help the war effort. Despite only having high school maths, Klara secured a wartime job coding with Princeton’s Office of Population Research. While her husband went to work on the Manhattan Project (war-time research on nuclear weapons), Klara become head of the Statistical Computing Group at Princeton until the end of the war. In peace time, she continued to program for many of the earliest computers, including the ENIAC, and trained others to do the same.

Meanwhile, ENIAC, the first digital computer, was being built in America for the war effort by John Mauchly and J. Presper Eckert. This innovative machine was, again, powered and programmed by a team of women. The original ENIAC wasn’t a computer like we have today, ones that stores program instructions in electronic memory. Instead, think of it as a machine with a series of huge plug boards rigged up to thousands of wires that needed to be manually pulled in and out. Needless to say, it was laborious.

ENIAC was set up to calculate ballistic trajectories for weapons, but it was the hard work and pluck of the original programmers, Jean Bartik, Betty Holberton, Kathleen Antonelli, Marlyn Meltzer, Ruth Teitelbaum and Frances Spence, who got this beast going. What’s more, all these women taught themselves how to operate the 150-foot tall machine as the engineers had no time for programming manuals or classes. They wrote the program by looking at ENIAC’s logical and electrical block diagrams, and created their own flowcharts and programming sheets, navigating the hundreds of wires and thousands of switches to place it on the ENIAC. These women were drawing on their mathematical ability, but I think the extra skill was their determination to get these machines to work for them.

Jean Bartik did once say that the ENIAC was a ‘son of a bitch’ to program, but these women were devoted to their work. On Valentine’s Day in 1946, Bartik and Betty Holberton were up late working on the machine ahead of a big press conference the next day. Reflecting on that evening, Bartik wrote ‘Most people consider Valentine’s Day a romantic day, but we never gave a thought that evening of romantic dinners. What we were thinking of was an all-important demonstration we were to run the next day for the world.’ The day came but no one was told about the work of these women. The lab only introduced the ENIAC’s hardware inventors – all men – to the press. Photos from the event show the women’s faces snapped in the background, uncredited. After the big reveal, there was a celebratory dinner with many invited guests. The women were sent home. It wasn’t until 1985 when Kathy Kleiman, an undergraduate programmer at Harvard, was searching for female role models that the story of the women of ENIAC was rediscovered. They had to wait until they were seventy years old to be recognised as the world’s first computer programmers. And yet we still benefit from technologies that they helped to develop: Klara von Neumann, for example, applied her understanding of ENIAC technology to make huge breakthroughs in weather forecasting.

The Turing Test

These wartime computers proved that machines could be more powerful than first imagined. In 1950, at the dawn of the digital age, Mind published Alan Turing’s seminal paper ‘Computing Machinery and Intelligence’, in which he posed the question, ‘Can machines think?’ Instead of trying to define the terms ‘machine’ and ‘think’, Turing outlines a different method derived from a Victorian parlour amusement called the imitation game. This later became known as the Turing test, and it demonstrated how a human may be unable to distinguish machine from another human being.

The Turing test involves three participants: a computer who answers questions, a human who answers questions and another human who acts as the interrogator. Using only a keyboard and a display screen, the interrogator will ask both human and computer a series of wide-ranging questions to determine which is the computer. If the interrogator is unable to distinguish the human from the computer then it is considered an intelligent-thinking entity and has passed the test.

I subscribe to the belief that the Turing test doesn’t actually assess whether a machine is intelligent, more that we’re willing to accept it as intelligent. As Turing himself said in ‘Intelligent machinery’, a report he wrote for the National Physical Laboratory, ‘The idea of intelligence is itself emotional rather than mathematical. The extent to which we regard something as behaving in an intelligent manner is determined as much by our own state of mind and training as by the properties of the object under consideration.’ Turing’s paper had a huge impact in a variety of fields as questions about machine intelligence snowballed into those about a machine having emotions or soul. It prompted further lines of enquiry of philosophy, engineering, sociology, psychology and religion.

In Chapter Seven, we’ll explore how, just as Turing predicted, modern-day AI products are built to kid you into thinking they are real humans. You’ll find some pointers about how to develop the skills needed to better spot the difference!

Grace Hopper (1906–1992)

Meanwhile, back in America, John von Neumann was developing ever more complex computers, like the MARK 1. One of this project’s first programmers was Grace Hopper. As well as being in the navy reserves during the Second World War, Hopper was excellent with numbers, and she used her PhD in mathematics to become a ground-breaking programmer.

It was Grace who came up with the idea of including English words in computer programming language, rather than the complicated numbers and symbols that had, until then, been used as commands. At first, her mostly male colleagues scoffed and thought it was impossible. But Grace persisted, determined to make programming more accessible. She’s been quoted as saying, ‘If it’s a good idea, go ahead and do it. It’s much easier to apologise than it is to get permission’, and I can’t help but admire this attitude from a woman who knew that some people would see her gender as a reason to hold her back. In 1952, she successfully released her first compiler (a way to translate between human language and computer language) called A-O. This idea of the compiler is what many coding languages still use now – a mix of numbers and letters compiled into a version that the computer can make sense of. Then, in 1959, she led the development of one of the earliest coding languages, COBOL.

What I think is so critical about Grace’s role in AI is that she was trying to make programming more accessible by effectively being a translator of computer language, removing it from the clutches of the few men who knew the program language. She considered those programmers as ‘high priests’ who regarded themselves as the gatekeepers between ordinary people and computers. This meant that while she was making all these incredible breakthroughs in programming, she was not popular among her male peers. Later she said that ‘Someone learns a skill and works hard to learn that skill, and then if you come along and say, “You don’t need that, here’s something else that’s better”, they are going to be quite indignant.’ But once more, she persisted. Hopper also went on to become one of the key people building and developing the standards for other programming languages like FORTRAN – paving the way for my own personal hero Dorothy Vaughn (more on her in a moment).

Hopper played a critical role in computation and AI. In 1969, she received the Computer Science Man of the Year Award from the Data Processing Management Association, to which was added throughout her life a whole host of other medals and awards that no woman had ever received before. She was even featured in a 1967 issue of Cosmopolitan magazine about women in computing – no mean feat for that time.

LISP, John McCarthy (1927–2011) & Phyllis Fox (1923– )

Although Turing was already asking ‘can computers think?’, it wasn’t until 1955, two years after Turing’s untimely death, that a young computer scientist, John McCarthy, coined the term Artificial Intelligence.

A year later, McCarthy brought together a group for the first conference on AI, and this is really when the field as we understand it began to take shape. Sadly, this is also the point at which the AI world became overwhelmingly male. The conference was organised and attended only by men. (It’s not that women were not still making waves in this new AI arena – they were – but they were often sidelined and rarely recognised.)

In 1958, McCarthy created the LISP computer language, which is still used. But what many people don’t know is that the first interpreter and manual for LISP was written by a woman named Phyllis Fox. As Fox humbly commented in an interview with the Society for Industrial and Applied Mathematics, ‘Now, this was not because I was a great LISP programmer, but they never documented or wrote down anything, especially McCarthy. Nobody in that group ever wrote anything down. McCarthy was furious that they didn’t document the code, but he wouldn’t do it, either. So I learned enough LISP that I could write it and ask them questions and write some more. One of the people in the group was a student named Jim Slagel, who was blind. He learned LISP sort of from me, because I would read him what I had written and he would tell me about LISP and I would write some more. His mind was incredible.’

I’ve often been the only person in the room who would just get on with it and write something down to preserve it when the men wouldn’t. Phyllis Fox is an inspiration, not only as a woman coder, but also for reminding us of the importance of translators, preservers and teachers to the history of AI.

The 1960s and 1970s
DENDRAL & Georgia L. Sutherland

In the mid-1960s, a problem-solving AI project called DENDRAL was set up. It tried to use ideas from artificial intelligence to help chemistry labs and researchers identify unknown molecules by storing information about what they did know and then analysing those they didn’t in order to look for patterns and similarities. DENDRAL ran for decades, and a lot of the software and theory it used ended up being the basis for expert systems in the 1980s (more on that later.)

If you research DENDRAL, the names of four men (Edward Feigenbaum, Bruce G. Buchanan, Joshua Lederberg and Carl Djerassi) are repeatedly affiliated with the project. But it was a woman, Georgia L. Sutherland, who, as you can see from the published notes, wrote the program in LISP and was in fact the lead author on many of the papers.

Dorothy Vaughan (1910–2008)

Remember Grace Hopper and her work with FORTRAN, the programming language she helped design and build? Well, not only did FORTRAN become an incredibly important language for businesses and industries, it also played a huge part in the Space Race at NASA.

If you’ve watched the film or read the book Hidden Figures, which I suggest you do if you haven’t, you might already be familiar with the name Dorothy Vaughan. Vaughan worked at NASA during the segregated Jim Crow years, where women were employed as ‘computers’, calculating complicated maths formulae by hand. When Vaughan saw that NASA were about to implement new technology that would do the jobs of her team faster, thereby making them obsolete, she decided to teach herself FORTRAN. She got the system working and then passed this knowledge on to her team, teaching them all that she’d learned. In doing so, she was able to make sure these women were skilled enough to be re-deployed to new jobs working on the most important part of the system.

Vaughan was the first ever African-American woman to be promoted to supervisor for the west computer group, the segregated female computing team. She led the electric computing department there and stayed at NASA until 1971.

Rather than feeling threatened by the machines, Dorothy embraced them, and with the confidence and foresight to predict the next wave of technology, she ensured that she and her team were primed and had the skills to keep their jobs and stay ahead of the machines.

Unimate

Tech wasn’t only being used for space. In 1961, Unimate was the first robot built for manufacturing and was used to automate repetitive tasks at a General Motors plant. This was the first time the idea of stored memory (early AI) was used in industry, instead of in a lab or a research setting. It prompted a wave of robots being built through the 1960s and 1970s, notably the WABOT in Japan, which was the first robot built to resemble a human.

Kathleen Booth (1922– ) & Karen Spärck Jones (1935–2007)

The late 1960s and early 1970s was a real boomtime for AI, and Karen Spärck Jones was part of it. Instead of focusing on the growing field of coding, she taught computers to understand the human language. This is called natural language processing (NLP) and it is critical for the idea of computer intelligence. Before Jones, little work had been undertaken to get computers to understand spoken or written English, although as early as the 1950s a brilliant woman named Kathleen Booth first imagined some of these concepts. Jones, however, was responsible for one of the biggest breakthroughs in NLP with something called IDF (inverse document frequency), a way of calculating how important a term is in a text. Her 1972 paper laid the groundwork of IDF, and this became the basis for almost all the search engines that we use now.

Like so many of the women featured in this chapter, what I admire and think is so cool about Jones is that she was a self-taught programmer – her background was in philosophy and history. I can’t help but think that her immersion in these disciplines sparked her approach to computer science, which focused not only on how to make computers understand humans, but also its social impact.

She also came up with the phrase: ‘Computing is too important to be left to men.’ Karen Spärck Jones, I salute you!

The First AI Winter

Okay, so I’ve rounded up some of the prime events that led towards the development of AI, but in truth it’s not as streamlined as it might appear on the page. Once the quest for artificial intelligence was truly underway, expectations snowballed, making the lack of advancement and results by the end of the 1970s dispiriting. Problems with progressing centred around two basic limitations: there was not enough RAM memory, making the processing speeds poor. This led to a lack of data suitable to train the machines. Progress slowed and research money was pulled. What followed came to be known as the ‘AI Winter’, which we can roughly date as beginning in the mid-1970s and ending in 1980.

The 1980s
Expert Systems

The emergence of expert systems in the 1980s went some way to address this. An expert system is a computer system that has an in-built decision-making ability. They work by using pre-programmed bodies of knowledge to reason their way through complex problems. These were the first truly successful forms of AI software and focused on narrow tasks, such as playing chess.

All around the world, expert systems were developed and quickly adopted by competitive corporations – just like Georgia Sutherland and DENDRAL. This meant that the primary focus of AI research turned to accumulating comprehensive quantities of knowledge from various experts. It didn’t take long before AI’s commercial value started to be realised, and investment into it began to flow again.

Deep Learning

Alongside the expert systems in the 1980s, another method of AI was being developed. Deep learning is a type of machine learning that tries to mimic the way humans learn through experience. You’ll learn more about all the technical parts of this in the next chapter. Some of the core ideas from machine learning – the idea that a computer can learn to make decisions from identifying data patterns – can be traced to those early days in the 1950s, but deep learning brought machine learning to a new level. It was first developed by mathematical psychologist David Rumelhart and his doctoral advisor, physicist John Hopfield. They were later joined by Geoffrey Hinton, and it was their idea to train machines by mimicking the design of the brain with neural networks. Deep learning is still best practice for much of AI in the twenty-first century.

Deep Thought

Around this time, many people were still engrossed in how AI could work with humans, rather than just for them. One of the most common ways to explore this was through games. Engineers and computer scientists wanted to know: could a computer understand context sufficiently enough to play a game with a human? One of the most famous examples of this is Deep Thought, a chess program.

In 1988, Deep Thought beat its first grandmaster (someone ranked in the highest level of chess playing), marking a real turning point for AI. Within a decade, IBM’s chess playing application, Deep Blue, beat world-reigning chess champion, Garry Kasparov. The machine was capable of evaluating up to 200 million positions a second. But could it think strategically? The answer was such a resounding YES that Kasparov believed a human being had to be behind the controls. He also thought that the human was, of course, a man. In fact, Kasparov famously said, ‘Women, by their nature, are not exceptional chess players: they are not great fighters.’ Little did he know just how many women had already contributed to the development of the machine that had just shown him up.

By 1985, a billion dollars had been spent on AI. New, faster computers convinced American and British governments to start funding AI research again. But with the widespread introduction of desktop computers, it soon became apparent that expert systems were too costly to maintain. In comparison to desktops, expert systems were difficult to update and still didn’t learn. In 1987, the Defence Advanced Research Projects Agency (DARPA) concluded AI would not evolve sufficiently and instead chose to invest its funds in projects they decided would yield better results. A second, though this time much shorter, AI Winter began.

Leslie P. Kaelbling (1961– ) & Cynthia Breazeal (1967– )

In the late 1980s and 1990s, while AI had been toppled from its position as the golden child of the tech world, two women were nonetheless quietly making strides in developing it.

Leslie Pack Kaelbling is widely regarded as one of the leaders of AI in the 1980s and 1990s. She worked on the idea of reinforcement learning, which is one of three key areas of machine learning (the other two are supervised and unsupervised machine learning – more on this in Chapter Three). This was a departure from deep learning because machine learning is concerned with a computer learning from a set of data, whereas reinforcement learning learns from trial and error. It was Kaelbling who figured out how to use this for improving robot navigation – going on to win awards and edit leading journals in AI.

Meanwhile, Cynthia Breazeal was following a different line of enquiry. She wanted to know if machines could feel. This was called affective computing and Breazeal put this to the test by building a robot named KISMET. It was designed to recognise and simulate emotions and was one of the first ever robots able to demonstrate social and emotional interactions with humans. KISMET can perceive a variety of our emotions, and even gets uncomfortable when people get too close to it. Breazeal founded the JIBO company and continues to work on personalised robots today. Although Breazeal comes from a computer and engineering background, she also works in media, education and psychology.

Meanwhile, Going Online

You might be wondering at what point the Internet first appeared in the narrative of AI. The history of the Internet is a book in and of itself (see my notes on Claire L. Evans’ Broad Band in Chapter Eight), but the connections between the Internet and AI are difficult to break down since they’re now so intertwined. Think about how often people building algorithms have to contact other people to problem-solve, innovate and collaborate. But one way to look at this link is to consider how the Internet gave way to the collection of big data.

At its most simple, big data is the term given to datasets that are so big and complex that trends or patterns cannot be found without computational methods. A good example of this is how Facebook networks can be not only your friends, but also your friends of friends. Big data is critical for AI because it needs huge amounts of data to learn, and these datasets are often made available through the Web. It’s not that the Internet is big data per se, but rather it’s the way that big data has become so ubiquitous. When you’re browsing online, accepting cookies usually means you’re allowing a webpage to collect your data. So, although you are just one data point, the points from everyone who visits that page are pooled together into a dataset so large it can only be handled by computers.

This can all be traced back to a moment in 1969 when two devices first connected to each other over something called ARPANET. This is the earliest ancestor of what we now know as the Internet.

Before we go any further, I think it’s important to make a quick distinction between what is meant by the Web and the Internet. It’s pretty common to use them interchangeably, but technically the Internet is a collection of global networks while the Web is one way to access the information stored there. The Web can be traced back to 1991, but there are earlier examples of the same kind of systems from the eighties.

Jake Feinler (1931– ) & Radia Perlman (1951– )

Computer scientists have been interested in the idea of computers communicating with one another since the 1950s. This led to a development of networks – LAN and WAN, the names of which you might recognise from using your desktops or laptops when connecting to WIFI. I find it helpful to think of these networks like the underground roots of trees that spread out and connect to each other, sharing water or sugar and other chemical, hormonal and electrical signals. Computer networks work in the same way, but they share digital information instead. LAN is a local area network where computers are linked to each other in a single building or location. WAN is a wide area network where multiple LANS connect to each other across a larger geographic space. In the 1980s, both these systems were riddled with issues, but two amazing women set out to conquer them.

Elizabeth Feinler, known as Jake, solved a huge problem with WANs. One of the most famous and successful early WANs was ARPANET, which started out linking several university and military LANs to one another. But in these early days, WANs had great difficulty navigating – imagine having no search engines, no way of knowing how to find the person or organisation you need. Along with her team, Jake solved this and progressed the advancement of the Internet. She went on to be part of the naming authority for the Internet, devising and developing top-level domain names, including .com, .us and .gov.

Next up is Radia Perlman, who devised something called the Spanning Tree Protocol. It allowed computers on LANs to connect to more complicated networks without looping back along a path, which would have ultimately broken the connection. This protocol still forms the basis of many networks today.

Margaret Boden (1936– )

Margaret Boden is a Research Professor of Cognitive Science at the University of Sussex and an expert in the intersection of AI, psychology and philosophy.

She published her first article about AI in 1972, and has since led the way when it comes to researching about the intersection of computer science and the human mind. Stressing the importance of celebrating creativity, she continues to be a powerful voice advocating for the necessity of interdisciplinary research in AI, which means she is essentially a trailblazer for how I think about interacting with, and getting involved in, AI. She also thinks through the possibilities of machine creativity, with a special focus on what AI might mean for art in the twenty-first century.

Wendy Hall (1952– )

Dame Wendy Hall is one of the people who worked on a pre-Web hypermedia system. A hypermedia system links together different types of information like text or images in a non-linear way. Wendy led a team of computer scientists who collaborated on a hypermedia system called Microcosm at the University of Southampton. Many of the principles used in this system informed the Web as we know it today.

In 1987, Wendy and her colleague Gillian Lovegrove wrote the paper ‘Where have all the girls gone?’ to address the deficit of women working with computers. She continues to work at the forefront of research in multimedia and hypermedia. She is a Professor of Computer Science, Associate Vice President of International Engagement, and Executive Director of the Web Science Institute at the University of Southampton, and Managing Director of the Web Science Trust to name but a few roles that she currently undertakes. In these roles, she works tirelessly to ensure that the Web and the Internet are governed responsibly.

As the Web has become more intertwined with AI technologies, Wendy has brought her expertise to AI, and this is how we first met. Wendy was writing a review for the British government on AI. It was an exciting time, the whole community was abuzz, and there was an obvious need to get more people with artificial intelligence skills into the workforce. The many men and women consulted on ways to achieve this suggested that graduates who had completed undergraduate STEM degrees should be the ones encouraged to do a Master’s conversion course to AI. When I first heard this idea, I was uncomfortable. I’d not taken a STEM degree, and I knew that on average less than 25 per cent of nationwide STEM degrees were taken by women. I shared my concern with Wendy that this project might reduce the number of women who’d be able to take up this opportunity. Despite her own maths degree and computer science prowess, Wendy had in fact already started campaigning to open up this conversion course to people from other subjects. Since then, through the AI Sector Deal and the Office for AI she’s been able to get funding for non-STEM conversion, meaning that graduates from disciplines such as history, English and philosophy can access the course – richly diversifying the field of AI. There are more details about how you can apply in Chapter Eight.

Wendy has shattered many glass ceilings and has fought relentlessly to bring other women with her.

The 2000s
Fei-Fei Li (1976– ) & ImageNet

Fei-Fei Li worked at the intersection of computer science and neuroscience for years before founding ImageNet in 2006. ImageNet is an enormous database of labelled images used for object recognition. It was set up by Li because she recognised AI needed a database as well as algorithms and formulae. Building ImageNet through meetings with Christiane Fellbaum – one of the leaders of WordNet doing the same for language – it is now considered absolutely invaluable for AI research. ImageNet led to the deep learning revolution (the period where deep learning became the standard in AI) by tweaking training algorithms to make them perform much better. Essentially, it changed the AI game.

What I admire so much about Fei-Fei Li is the way she has always been invested in making AI more accessible and diverse, as well as considering its impact. In 2011, she started the non-profit A14ALL, which runs summer programs in AI for women, people of colour and low-income high-school kids.

Watson IBM Jeopardy

Remember IBM’s Deep Blue chess match? Well in 2010, IBM developed a machine that was trained by AI to play the game Jeopardy, learning the right and wrong answers from a bank of old Jeopardy questions. They named it Watson. The machine had no access to the Internet, so couldn’t search the answers online. Instead, it had to figure out the answers from what it already knew. It was part of a research plan to make the public more interested in AI and its potential. In 2011, Watson played two human contestants and won the game.

Voice Assistants

Another big moment in the use of everyday AI was the introduction of voice assistants. Siri was the first virtual AI assistant that could not only combine speech recognition and information retrieval but could also live conveniently in our pockets. Apple acquired it from its namesake company in 2010, then launched it as part of the iPhone the following year. In 2015, Amazon brought in Alexa, a device that responds to questions in real time, like Siri, but is activated by the sound of its name, rather than the push of a button. It was initially marketed as a way to make life easier: scheduling a calendar, playing music, connecting to other useful apps. But now Alexa can connect to smart home devices to switch lights on and off, and you might even hear her manipulated to rap on records.

DeepMind, Alibaba, Tencent, Baidu & OpenAI

DeepMind is a start-up AI research company founded in 2010 by Demis Hassabis, Shane Legg and Mustafa Suleyman in London. DeepMind is now run by a seriously awesome woman, Lila Ibrahim, and is a subsidiary of Alphabet, the most cash-rich company in the world (it even owns Google!). Like the chess games of the 1990s, the DeepMind team have been applying and developing AI techniques to the game Go – an ancient Chinese two-player boardgame, established over 3,000 years ago. In their computerised version, named AlphaGo, the number of possible board configurations is, to quote the DeepMind website, ‘More than the number of atoms in the known universe.’ By employing a whole range of AI techniques including reinforcement learning, AlphaGo won its first match against a human in 2015 before going on to win the Go summit in 2017. One of the wildest things is that after that 2017 summit, DeepMind released AlphaZero, which unlike IBM Watson was never taught from a trained dataset. Instead, it learned how to play from trial and error against itself, starting with totally random moves. This creates a way to win that human beings almost never do themselves. The DeepMind team see this as a critical step towards AI-augmented human creativity, and the potential of Artificial General Intelligence. Demis Hassabis is recognised as one of the biggest brains of his generation, so watch this space …

In the same year that AlphaZero was released, the Chinese government started pouring millions of dollars into AI development. Their focus was on helping organisations, companies and the military adapt and deploy AI, as well as integrating AI training into education. The Chinese government has plans to have built a $1 trillion AI industry by 2030. There are three major tech companies in China leading this AI revolution: Alibaba, Tencent and Baidu. These companies are described by journalists and governments alike as rivals to the American giants Amazon and Google. And what they’re building definitely challenges the idea that the US is ‘winning the AI race.’ In fact, a recent book by Kai-Fu Lee called AI Superpowers examines how the USA and China are developing AI, and argues that China has the upper hand. For example, the Chinese company Alibaba is currently the world’s largest e-commerce marketplace and has invested in seven AI research labs that will focus on machine learning, network security and natural language processing. And in 2018, Alibaba released robots that outperformed humans on the Stanford University reading test.

OpenAI

OpenAI is the AI research company founded by, among others, Elon Musk. In February of 2019, OpenAI created language software that was so good at learning human speech patterns and then generating text that they deemed it too risky to release the code to the public. This is because it could be misused, particularly for fake news and spamming people. But it’s important to note that currently machines can’t write perfect prose, nor can they have a true understanding of the world in order to be manipulative. What we need to be conscious of is that the people who are controlling them can’t either.

In August 2019, OpenAI decided to release a more curtailed version of the modeller that you can actually play around with yourself without having to use code. Try entering some text and seeing what the OpenAI bot refers back with by visiting https://talktotransformer.com, entering a prompt that the AI will complete.

I fed the machine Spice Girls lyrics, and below is the result. I’ll leave it to you to decide if we should have AI Spice on the reunion tour.

Prompt: ‘2 Become 1’, by the Spice Girls

Completion: Now come in here and show me how it’s done Now I’m watching a little history By the end of the night, I’ll know what’s going on And that, all I need are some deep thoughts I need some love.

When reading the history of AI, it’s important that we consider that the ideas of ‘winning’, ‘leading’ and ‘progressing’ in AI are not synonymous with ‘better’. We’ll explore more about this in Chapter Four, but good questions to keep in mind as we’re looking at more modern advancements are: what does it mean to be winning this race? What would you prioritise as a measure of success? What is the prize? And who gets to claim it and has been everybody been acknowledged? And even: who gets to decide what it means to be a leader of AI?

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